Dreaming of the Giro

fast.ai’s latest version of Practical Deep Learning for Coders Part 2 kicks off with a review of Stable Diffusion. This is a deep neural network architecture developed by Stability AI that is able to convert text into images. With a bit of tweaking it can do all sorts of other things. Inspired by the amazing videos created by Softology, I set out to generate a dreamlike video based on the idea of riding my bicycle around a stage of the Giro d’Italia.

Text to image

As mentioned in a previous post, Hugging Face is a fantastic resource for open source models. I worked with one of fast.ai’s notebooks using a free GPU on Google Colab. In the first step I set up a text-t0-image pipeline using a pre-trained version of stable-diffusion-v1-4. The prompt “a treelined avenue, poplars, summer day, france” generated the following images, where model was more strongly guided by the prompt in each row. I liked the first image in the second row, so I decided to make this the first frame in an initial test video.

Stable diffusion is trained in a multimodal fashion, by aligning text embeddings with the encoded versions corresponding images. Starting with random noise, the pixels are progressively modified in order to move the encoding of the noisy image closer to something that matches the embedding of the text prompt.

Zooming in

The next step was to simulate the idea of moving forward along the road. I did this by writing a simple two-line function, using fast.ai tools, that cropped a small border off the edge of the image and then scaled it back up to the original size. In order to generate my movie, rather that starting with random noise, I wanted to use my zoomed-in image as the starting point for generating the next image. For this I needed to load up an image-to-image pipeline.

I spent about an hour experimenting with with four parameters. Zooming in by trimming only a couple of pixels around the edge created smoother transitions. Reducing the strength of additional noise enhanced the sense of continuity by ensuring that that subsequent images did not change too dramatically. A guidance scale of 7 forced the model to keep following prompt and not simply zoom into the middle of the image. The number of inference steps provided a trade-off between image quality and run time.

When I was happy, I generated a sequence of 256 images, which took about 20 minutes, and saved them as a GIF. This produced a pleasing, constantly changing effect with an impressionist style.

Back to where you started

In order to make the GIF loop smoothly, it was desirable to find a way to return to the starting image as part of the continuous zooming in process. At first it seemed that this might be possible by reversing the existing sequence of images and then generating a new sequence of images using each image in the reversed list as the next starting point. However, this did not work, because it gave the impression of moving backwards, rather than progressing forward along the road.

After thinking about the way stable diffusion works, it became apparent that I could return to the initial image by mixing it with the current image before taking the next step. By progressively increasing the mixing weight of the initial image, the generated images became closer to target over a desired number of steps as shown below.

Putting it al together produced the following video, which successfully loops back to its starting point. It is not a perfect animation, because the it zooms into the centre, whereas the vanishing point is below the centre of the image. This means we end up looking up at the trees at some points. But overall it had the effect I was after.

A stage of the Giro

Once all this was working, it was relatively straightforward to create a video that tells a story. I made a list of prompts describing the changing countryside of an imaginary stage of the Giro d’Italia, specifying the number of frames for each sequence. I chose the following.

[‘a wide street in a rural town in Tuscany, springtime’, 25],

[‘a road in the countryside, in Tuscany, springtime’,25],

[“a road by the sea, trees on the right, sunny day, Italy”,50],

[‘a road going up a mountain, Dolomites, sunny day’,50],

[‘a road descending a mountain, Dolomites, Italy’,25],

[‘a road in the countryside, cypress trees, Tuscany’,50],

[‘a narrow road through a medieval town in Tuscany, sunny day’,50]

These prompts produced the video shown at the top of this post. The springtime blossom in the starting town was very effective and the endless climb up into the sunlit Dolomites looked great. For some reason the seaside prompt did not work, so the sequence became temporarily stuck with red blobs. Running it again would make something different. Changing the prompts offered endless possibilities.

The code to run this appears on my GitHub page. If you have a Google account, you can open it directly in Colab and set the RunTime to GPU. You also need a free Hugging Face account to load the stable diffusion pipelines.

Learning the language of the Giro


Computers are becoming ever better at natural language processing (NLP). I set up a neural network as a language model and loaded a set of pre-trained weights on my MacBook Pro, to see what it could do. After priming it with some text about the Giro d’Italia, this is what the model produced as a continuation.

Priming text: The Giro d’ Italia (Italian pronunciation: [ˈdʒiːro diˈtaːlja]; English: Tour of Italy; also known as the Giro) is an annual multiple-stage bicycle race primarily held in Italy, while also occasionally passing through nearby countries. The first race was organized in 1909 to increase sales of the newspaper La Gazzetta dello Sport; however it is currently run by RCS Sport. The race has been held annually since its first edition in 1909, except when it was stopped for the two world wars. As the Giro gained prominence and popularity the race was lengthened, and the peloton expanded from primarily Italian participation to riders from all over the world.

Computer generated continuation:  the race was won by the italian rider , giovanni di u_n , who won the race in the first leg of the race . the race was won by italian rider giovanni u_n , who won the race by a margin of two lengths .
= = = world tour = = =
the tour de france was the first of the tour de france . the tour de france was won by the reigning world champion , the reigning world champion , who had won the tour de france in the previous year ‘s race …

The output may not make a lot of sense, but the point is that it looks like English (in lower case). The grammar is reasonable, with commas, fullstops and a header inserted in  a logical way. Furthermore, the model has demonstrated some understanding of the context by suggesting that the Giro could be won by an Italian ride called Giovanni. The word “u_n” stands for unknown, which is consistent with the idea that an Italian surname may not be a familiar English word. It turns out that a certain Giovanni Di Santi raced against Fausto Coppi (pictured above) in the 1940 Giro, though he did not win the first stage. In addition to this, the model somehow knew that the Giro, in common with the Tour the France, is a World Tour event that could be won by the reigning world champion.

I found this totally amazing. And it was not a one off: further examples on random topics are included below. This neural network is just an architecture, defining a collection of matrix multiplications and transformations, along with a set of connection weights. Admittedly there are a lot of connection weights: 115.6 million of them, but they are just numbers. It was not explicitly provided with any rules about English grammar or any domain knowledge.

How could this possibly work?

In machine learning, language models are assessed on a simple metric: accuracy in predicting the next word of a sentence. The neural network approach has proved to be remarkably successful. Given enough data and a suitable architecture, deep learning now far outstrips traditional methods that relied on linguistic expertise to parse sentences and apply grammatical rules that differ across languages.

I was experimenting with an AWD-LSTM model originally created by Stephen Merity. This is a recurrent neural network (RNN) with three LSTM layers that include dropout. The pre-trained weights for the wt103 model were generated by Jeremy Howard of fast.ai, using a large corpus of text from Wikipedia.

Jeremy Howard converted the Wikipedia text into tokens. A tokeniser, such as spaCy,  breaks text into words and punctuation, resulting in a vocabulary of tokens that are indexed as integers. This allows blocks of text to be fed into the neural network as lists of numbers. The outputs are numbers that can be converted back into the predicted words.

The wt103 model includes a linear encoder that creates embeddings of word tokens. These are passed through three LSTM layers whose states are able to retain a memory of previous words or context. The result is passed through a decoder, employing the same weights as the encoder, to produce a softmax output that can be treated as a set of probabilities, across the vocabulary, to predict the next word token. Special forms of dropout were employed, as described in the paper, to limit overfitting and make the model more robust.

The network was trained by minimising cross-entropy loss using stochastic gradient descent. A technique called back propagation through time (BPTT) introduced some random variation into the length of the blocks of text employed in each training batch. The connection weights were iteratively improved towards optimal values.

OK, but so what?

Impressive as it may be for a neural network to spout highly plausible but nonsensical English, what possible use could this have? Well, suppose you want to automate the separation of texts into different categories, for example, distinguish different authors or determine the sentiment of tweets or predict the reaction of the stock market to news items or identify the sentiment of movie reviews. Fine-tuning a neural network that already has a good knowledge of English provides a huge advantage over starting from scratch. In lesson 10 of the fast.ai course, Jeremy Howard demonstrates how this approach can be used to achieve state of the art results on the widely used IMDB movie review data set.

Further examples

In the first example, the network finishes the sentence about a hurricane in perfect English.  Next, it comes up with the name of a critic (actually a rock music critic rather than a book reviewer, but heh!) and it makes use of its LSTM to open and close quotation marks. In the bean example, it amazingly starts describing flowers and correctly converts their dimensions from centimetres to inches!

My code can be found here.

Priming text: Hurricane Ivan was a large long Cape Verde hurricane that caused widespread damage in the Caribbean and United States The cyclone was

Computer generated continuation:  the first hurricane to strike the united states since hurricane katrina in 2005 .

Priming text: The book was widely acclaimed by the critics.

Computer generated continuation: the book was published in the united states by the american library association on june 1 , 2006 .

= = = critical response = = =

the book received mixed reviews from critics . the new york times reviewer robert christgau called it ” a great book ” and ” a great book ” . he praised the book ‘s ” strong , strong , and well – written ” prose , and the ” excellent ” prose .

Priming text: Most varieties of bean grow either as an erect bush or as a climbing plant, but a few important kinds are of intermediate form. Dwarf and semiclimbers are grown extensively. When the climbing type is grown for its immature pods, artificial supports are necessary to facilitate harvesting. Varieties differ greatly in size, shape, colour, and fibrousness or tenderness of the immature pods. In general, varieties grown for dry mature seeds produce pods that are too fibrous to be eaten at any state of development. Most edible-podded beans produce relatively low yields of mature seeds, or seeds that are of low eating quality. Seed colours range from white through green, yellow, tan, pink, red, brown, and purple to black in solid colours and countless contrasting patterns. Seed shapes range from nearly spherical to flattened, elongated, and kidney-shaped. Pods are of various shades of green, yellow, red, and purple and splashed with red or purple; pod shapes range from flat to round, smooth to irregular, and straight to sharply curved; length ranges from 75 to 200 millimetres (3 to 8 inches) or more.

Computer generated continuation:  the most common form of the flower is the flower spike , which is a characteristic of the flower spike . the flower is made up of a series of small , cylindrical , cylindrical , cylindrical , cylindrical stems that are up to 10 cm ( 3.9 in ) long and 2.5 cm ( 0.98 in ) wide . the flower spikes are arranged in a series of three or four pairs of flowers